What metric frameworks does this skill support?

It supports industry-standard frameworks including AARRR (Acquisition, Activation, Retention, Referral, Revenue) for growth, North Star for strategic alignment, and HEART for user experience measurement.

How does it help with different product stages?

The skill provides specific KPI guidance tailored to your product's maturity, whether you are focusing on activation in pre-PMF, conversion velocity in Growth, or retention depth in Mature stages.

Does it help avoid common reporting mistakes?

Yes, it identifies specific anti-patterns such as tracking vanity metrics, ignoring seasonality, or reporting blended metrics that hide important segment differences.

Can this skill help me analyze raw event data?

Yes, it includes a metrics_calculator script that processes CSV data to generate retention matrices, cohort analysis, and funnel conversion reports in both text and JSON formats.

Product Analytics & KPI Strategy

Name: Product Analytics & KPI Strategy
Author: alirezarezvani

byalirezarezvani

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9,958

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Analytics & Monitoring

Defines, tracks, and interprets product metrics across discovery, growth, and mature stages to drive data-informed decisions.

This skill empowers product teams to establish robust measurement frameworks like AARRR, North Star, and HEART within their development workflow. It provides structured guidance on selecting stage-appropriate KPIs—from activation rates in pre-PMF to net revenue retention in mature products—while offering practical tools for cohort analysis, retention curve interpretation, and dashboard design. By identifying common anti-patterns like vanity metrics or dashboard overload, it ensures that every metric tracked leads to actionable product improvements and strategic alignment.

Key Features

01Metric framework implementation for AARRR, North Star, and HEART models

02Structured dashboard design principles for executive and feature-level visibility

039,958 GitHub stars

04Automated cohort and retention analysis via built-in CLI utilities

05Interpretative guidance for distinguishing signal from noise in feature adoption trends

06Stage-appropriate KPI definitions for pre-PMF, growth, and mature products

Use Cases

01Performing cohort analysis to identify friction points in the user onboarding journey

02Building an executive dashboard that prioritizes directional health metrics over vanity stats

03Defining a metric hierarchy for a new feature launch to measure adoption and depth

Key Features

01Metric framework implementation for AARRR, North Star, and HEART models

02Structured dashboard design principles for executive and feature-level visibility

039,958 GitHub stars

04Automated cohort and retention analysis via built-in CLI utilities

05Interpretative guidance for distinguishing signal from noise in feature adoption trends

06Stage-appropriate KPI definitions for pre-PMF, growth, and mature products

Use Cases

01Performing cohort analysis to identify friction points in the user onboarding journey

02Building an executive dashboard that prioritizes directional health metrics over vanity stats

03Defining a metric hierarchy for a new feature launch to measure adoption and depth